End of training

README.md

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+---
+library_name: transformers
+base_model: dmis-lab/biobert-base-cased-v1.1
+tags:
+- generated_from_trainer
+metrics:
+- accuracy
+- precision
+- recall
+- f1
+model-index:
+- name: Biobert_combo_v2
+  results: []
+---
+
+<!-- This model card has been generated automatically according to the information the Trainer had access to. You
+should probably proofread and complete it, then remove this comment. -->
+
+# Biobert_combo_v2
+
+This model is a fine-tuned version of [dmis-lab/biobert-base-cased-v1.1](https://huggingface.co/dmis-lab/biobert-base-cased-v1.1) on the None dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.1933
+- Accuracy: 0.924
+- Auc: 0.978
+- Precision: 0.938
+- Recall: 0.938
+- F1: 0.938
+- F1-macro: 0.919
+- F1-micro: 0.924
+- F1-weighted: 0.924
+
+## Model description
+
+More information needed
+
+## Intended uses & limitations
+
+More information needed
+
+## Training and evaluation data
+
+More information needed
+
+## Training procedure
+
+### Training hyperparameters
+
+The following hyperparameters were used during training:
+- learning_rate: 2e-05
+- train_batch_size: 8
+- eval_batch_size: 8
+- seed: 42
+- gradient_accumulation_steps: 4
+- total_train_batch_size: 32
+- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
+- lr_scheduler_type: linear
+- num_epochs: 5
+- mixed_precision_training: Native AMP
+
+### Training results
+
+| Training Loss | Epoch  | Step | Validation Loss | Accuracy | Auc   | Precision | Recall | F1    | F1-macro | F1-micro | F1-weighted |
+|:-------------:|:------:|:----:|:---------------:|:--------:|:-----:|:---------:|:------:|:-----:|:--------:|:--------:|:-----------:|
+| 0.4506        | 0.2661 | 500  | 0.2929          | 0.883    | 0.944 | 0.902     | 0.91   | 0.906 | 0.876    | 0.883    | 0.883       |
+| 0.2846        | 0.5323 | 1000 | 0.2606          | 0.897    | 0.957 | 0.904     | 0.934  | 0.918 | 0.89     | 0.897    | 0.897       |
+| 0.2462        | 0.7984 | 1500 | 0.2316          | 0.907    | 0.966 | 0.915     | 0.938  | 0.926 | 0.901    | 0.907    | 0.907       |
+| 0.2337        | 1.0644 | 2000 | 0.2297          | 0.91     | 0.967 | 0.926     | 0.928  | 0.927 | 0.904    | 0.91     | 0.91        |
+| 0.21          | 1.3305 | 2500 | 0.2212          | 0.911    | 0.97  | 0.934     | 0.922  | 0.928 | 0.906    | 0.911    | 0.911       |
+| 0.2033        | 1.5967 | 3000 | 0.2181          | 0.913    | 0.972 | 0.925     | 0.935  | 0.93  | 0.908    | 0.913    | 0.913       |
+| 0.2029        | 1.8628 | 3500 | 0.2109          | 0.916    | 0.974 | 0.92      | 0.946  | 0.933 | 0.91     | 0.916    | 0.915       |
+| 0.1948        | 2.1288 | 4000 | 0.2030          | 0.921    | 0.975 | 0.94      | 0.931  | 0.935 | 0.916    | 0.921    | 0.921       |
+| 0.1812        | 2.3949 | 4500 | 0.2093          | 0.918    | 0.974 | 0.933     | 0.935  | 0.934 | 0.913    | 0.918    | 0.918       |
+| 0.1822        | 2.6611 | 5000 | 0.1996          | 0.92     | 0.976 | 0.933     | 0.939  | 0.936 | 0.916    | 0.92     | 0.92        |
+| 0.1818        | 2.9272 | 5500 | 0.1945          | 0.923    | 0.977 | 0.936     | 0.94   | 0.938 | 0.918    | 0.923    | 0.923       |
+| 0.1707        | 3.1932 | 6000 | 0.1955          | 0.923    | 0.977 | 0.946     | 0.929  | 0.937 | 0.919    | 0.923    | 0.923       |
+| 0.1635        | 3.4593 | 6500 | 0.2019          | 0.922    | 0.977 | 0.935     | 0.939  | 0.937 | 0.917    | 0.922    | 0.922       |
+| 0.1747        | 3.7255 | 7000 | 0.1983          | 0.923    | 0.977 | 0.931     | 0.945  | 0.938 | 0.918    | 0.923    | 0.923       |
+| 0.1735        | 3.9916 | 7500 | 0.1956          | 0.923    | 0.978 | 0.936     | 0.941  | 0.938 | 0.919    | 0.923    | 0.923       |
+| 0.1646        | 4.2576 | 8000 | 0.1994          | 0.921    | 0.977 | 0.933     | 0.94   | 0.937 | 0.916    | 0.921    | 0.921       |
+| 0.1616        | 4.5238 | 8500 | 0.1925          | 0.924    | 0.978 | 0.942     | 0.934  | 0.938 | 0.919    | 0.924    | 0.924       |
+| 0.1615        | 4.7899 | 9000 | 0.1933          | 0.924    | 0.978 | 0.938     | 0.938  | 0.938 | 0.919    | 0.924    | 0.924       |
+
+
+### Framework versions
+
+- Transformers 4.53.2
+- Pytorch 2.6.0+cu124
+- Datasets 2.14.4
+- Tokenizers 0.21.2

config.json

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+{
+  "architectures": [
+    "BertForSequenceClassification"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "classifier_dropout": null,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 768,
+  "id2label": {
+    "0": "fake",
+    "1": "real"
+  },
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "label2id": {
+    "fake": 0,
+    "real": 1
+  },
+  "layer_norm_eps": 1e-12,
+  "max_position_embeddings": 512,
+  "model_type": "bert",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "pad_token_id": 0,
+  "position_embedding_type": "absolute",
+  "problem_type": "single_label_classification",
+  "torch_dtype": "float32",
+  "transformers_version": "4.53.2",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 28996
+}

model.safetensors

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special_tokens_map.json

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+{
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+  "mask_token": "[MASK]",
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "unk_token": "[UNK]"
+}

tokenizer_config.json

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+{
+  "added_tokens_decoder": {
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+      "content": "[PAD]",
+      "lstrip": false,
+      "normalized": false,
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+      "single_word": false,
+      "special": true
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+  "clean_up_tokenization_spaces": true,
+  "cls_token": "[CLS]",
+  "do_basic_tokenize": true,
+  "do_lower_case": true,
+  "extra_special_tokens": {},
+  "mask_token": "[MASK]",
+  "model_max_length": 1000000000000000019884624838656,
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+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "strip_accents": null,
+  "tokenize_chinese_chars": true,
+  "tokenizer_class": "BertTokenizer",
+  "unk_token": "[UNK]"
+}

training_args.bin

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